Hand Bone Loss in Patients with Psoriatic Arthritis: Posthoc Analysis of IMPACT II Data Comparing Infliximab and Placebo
Bibliographic record
Abstract
OBJECTIVE: In rheumatoid arthritis (RA), anti-tumor necrosis factor (anti-TNF) treatment is shown to reduce but not to arrest the rate of hand bone loss. This has not been assessed in psoriatic arthritis (PsA). Our objective was to examine changes in cortical hand bone density in patients with PsA treated with placebo or infliximab (IFX). METHODS: Patients in IMPACT II (Induction and Maintenance Psoriatic Arthritis Clinical Trial 2) were randomized to placebo or IFX. After Week 24, all received IFX. In a subset of 120 patients, cortical hand bone density was assessed at Weeks 0, 24, and 54 by digital X-ray radiogrammetry (dxr-BMD) on the same radiographs scored for joint damage. RESULTS: Changes from baseline to 24 weeks in dxr-BMD were -0.30% (SD 1.1%) in the placebo group and -0.08% (SD 1.4%) in the IFX group (p = 0.63). Between baseline and 54 weeks the changes were -0.71% (SD 2.1%) in the placebo group and 0.15% (SD 1.7%) in the IFX group (p = 0.07), and between 24 and 54 weeks -0.41% (SD 1.4%) and 0.23% (SD 0.8%), respectively (p = 0.05). No significant correlation was found between change in dxr-BMD and radiographic damage. CONCLUSION: This pilot study indicates that hand bone loss in PsA patients treated with anti-TNF can be arrested. Assessment of hand bone density may thus be a potential outcome measure for bone involvement and a response variable to treatment in PsA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".